import argparse
from copy import deepcopy
import logging
from os import device_encoding
from pathlib import Path
from typing import ForwardRef
import torch.nn as nn
import torch
import logging
import yaml,sys

try:
    import thop  # for FLOPs computation
except ImportError:
    thop = None

LOGGER = logging.getLogger(__name__)

FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH


from models.yolo import Detect
from models.yolo import parse_model
from models.common import *
from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general import check_yaml, make_divisible, print_args, set_logging
from utils.plots import feature_visualization
from utils.torch_utils import copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, \
    select_device, time_sync



class Detect(nn.Module):
    '''
    This is for the detection layer in the yolo layer
    '''
    stride = None  # strides computed during build
    onnx_dynamic = False  # ONNX export parameter

    def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
        super().__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        self.anchor_grid = [torch.zeros(1)] * self.nl  # init anchor grid
        self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
        self.inplace = inplace  # use in-place ops (e.g. slice assignment)

    def forward(self, x):
        z = []  # inference output
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            if not self.training:  # inference
                if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)

                y = x[i].sigmoid()
                if self.inplace:
                    y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
                    y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
                    xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, y[..., 4:]), -1)
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x)

    def _make_grid(self, nx=20, ny=20, i=0):
        d = self.anchors[i].device
        yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)])
        grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
        anchor_grid = (self.anchors[i].clone() * self.stride[i]) \
            .view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
        return grid, anchor_grid

class Model(nn.Module):

    def __init__(self,cfg='yolov5s.yaml',ch=3,nc=None,anchors=None):
        super().__init__()
        if isinstance(cfg,dict):
            self.yaml =cfg
        else:
            import yaml
            self.yaml_file = Path(cfg).name
            with open(cfg,errors='ignore') as f:
                self.yaml=yaml.safe_load(f)  # model dict
        
        # define model
        #print(self.yaml.keys())
        ch = self.yaml['ch'] = self.yaml.get('ch',ch) # Input channels
        print(self.yaml.keys())
        print(ch)

        if nc and nc !=self.yaml['nc']:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml['nc'] =nc  # overriding yaml value

        if anchors:
            LOGGER.info(f'Overriding model.yaml with anchors with anchors={anchors}')
            self.yaml['anchors'] = round(anchors)

        self.model, self.save = parse_model(deepcopy(self.yaml),ch=[ch])
        #print(self.model)
        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
        self.inplace = self.yaml.get('inplace', True)


        # Build strides, anchors
        m = self.model[-1]   #  Detect()
        if isinstance(m,Detect):
            s=256
            m.inplace = self.inplace
            m.stride = torch.tensor( [s / x.shape[-2] for x in self.forward(torch.zeros(1,ch,s,s))])  # Forward
            m.anchors /= m.stride.view(-1,1,1)
            check_anchor_order(m)
            self.stride=m.stride
            self._initialize_biases()

        # Init weights, biases

        initialize_weights(self)
        self.info()
        LOGGER.info('')

    def forward(self,x,augment=False,profile=True,visualize=False):
        if augment:
            return self._forward_augment(x)  # Augmented inference, None

        return self._forward_once(x,profile,visualize)

    def _forward_augment(self,x):
        img_size= x.shape[-2:]  # hegith, width
        s = [1, 0.83, 0.67]  # scales
        f = [None, 3, None]
        y=[]  # outputs
        for si, fi in zip(s, f):
            xi= scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
            yi= self._forward_once(xi)[0] # forward
            yi = self._descale_pred(yi,fi,si,img_size)
            y.append(yi)

        y = self._clip_augmented(y)
        return torch.cat(y, 1), None  # augmented inference, train



    def _forward_once(self,x, profile=False, visualize=False):
        y, dt = [], [] # outputs
        for m in self.model:
            if m.f !=-1 # if not from the previous layer
            x= y[m.f] if isinstance(m.f, int) else [x if j== -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m,x,dt)

            x=m(x) # run
            y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x,m.type,m.i,save_dir= visualize)

            return x

    def _descale_pred(self, p, flips, scale, img_size):
        # de-scale predictions following augmented inference (inverse operation)
        if self.inplace:
            p[..., :4] /= scale  # de-scale
            if flips == 2:
                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
            elif flips == 3:
                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
        else:
            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
            if flips == 2:
                y = img_size[0] - y  # de-flip ud
            elif flips == 3:
                x = img_size[1] - x  # de-flip lr
            p = torch.cat((x, y, wh, p[..., 4:]), -1)
        return p

    def _clip_augmented(self, y):
        # Clip YOLOv5 augmented inference tails
        nl = self.model[-1].nl  # number of detection layers (P3-P5)
        g = sum(4 ** x for x in range(nl))  # grid points
        e = 1  # exclude layer count
        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
        y[0] = y[0][:, :-i]  # large
        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
        y[-1] = y[-1][:, i:]  # small
        return y

    def _profile_one_layer(self,m,x,dt):
        c = isinstance(m, Detect)  # is final layer, copy input as inplace fix
        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose = False)[0] / 1E9 * 2 id thop else o # FLOPs
        t = time_sync()
        for _ in range(10):
            m(x.copy() if c else x)
        dt.append((time_sync() - t) * 100)
        if m == self.model[0]:
            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  {'module'}")
        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
        if c:
            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
        # https://arxiv.org/abs/1708.02002 section 3.3
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Detect() module
        for mi, s in zip(m.m, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
    def _print_biases(self):
        m = self.model[-1]  # Detect() module
        for mi in m.m:  # from
            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
            LOGGER.info(
                ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
        LOGGER.info('Fusing layers... ')
        for m in self.model.modules():
            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                delattr(m, 'bn')  # remove batchnorm
                m.forward = m.forward_fuse  # update forward
        self.info()
        return self

    def autoshape(self):  # add AutoShape module
        LOGGER.info('Adding AutoShape... ')
        m = AutoShape(self)  # wrap model
        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
        return m

    def info(self, verbose=False, img_size=640):  # print model information
        model_info(self, verbose, img_size)

    def _apply(self, fn):
        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
        self = super()._apply(fn)
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):
            m.stride = fn(m.stride)
            m.grid = list(map(fn, m.grid))
            if isinstance(m.anchor_grid, list):
                m.anchor_grid = list(map(fn, m.anchor_grid))
        return self

def parse_model(d, ch):  # model_dict, input_channels(3)

    LOGGER.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
    anchors, nc, gd, gw = d['anchors'],d['nc'],d['depth_multiple'],d['width_multiple']
    na = len(anchors[0])//2 if isinstance(anchors,list ) else anchors
    no = na*(nc+5) # Number of outputs = anchors* (classes +5)

    layers, save, c2 = [],[], ch[-1] # layers, savelist, ch out
    for i, (f,n,m,args) in enumerate(d['backbone']+d['head']): # from, number, modeul, args
        m = eval(m) if isinstance(m,str) else m # evaluating m

        for j, a in enumerate(args):
            try:
                args[j] = eval(a) if isinstance(a,str) else a # eval strings

            except NameError:
                pass

        n = n_ = max(round(n * gd),1) if n >1 else n # depth gain
        if m in [Conv,GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d,
                  Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:
            c1,c2 = ch[f],args[0]

            if c2 != no:
                c2=make_divisible(c2 *gw,8)

            args=[c1,c2,*args[1:]]
            if m in[BottleneckCSP,C3,C3TR, C3Ghost]:
                args.insert(2,n)  # Insert number of repeates
                n=1

        elif m is nn.BatchNorm2d:
            args=[ch[f]]
        elif m is Concat:
            c2= sum([ch[x] for x in f])     # output after concatenating the feature maps from the different layers
        elif m is Detect:
            args.append([ch[x] for x in f])
            #print("Inside detect")
            #print(args)
            if isinstance(args[1],int):
                args[1] =[list(range(args[1]*2))]*len(f) 
        elif m is Contract:
            c2=ch[f]*args[0] **2		
        elif m is Expand:
            c2= ch[f] //args[0] **2
        
        else:
            c2=ch[f]


        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n>1 else m(*args)
        t = str(m)[8:-2].replace('__main__.','')  # Module type
        np = sum([x.numel() for x in m_.parameters()])  # Number of params
        m_.i,m_.f,m_.type,m_.np = i,f,t,np # attach index, 'from' index, type, number params
        LOGGER.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n_, np, t, args)) # Printing
        save.extend(x%i for x in ([f] if isinstance(f,int) else f) if x!= -1)
        layers.append(m_)
        if i==0:
            ch=[]
        ch.append(c2)
        
    return nn.Sequential(*layers) , sorted(save)

model = Model()

if __name__ =='__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument('--cfg',type=str,default='yolo5s.yaml',help='model.yaml')
	parser.add_argument('--device',default='',help='cuda device')
	parser.add_argument('--profile',action='store_true',help='profile model speed')
	opt = parser.parse_args()
	opt.cfg = check_yaml(opt.cfg)
	print_args(FILE.stem,opt)
	set_logging()
	device_encoding = select_device(opt.device)

	# create model
	model = Model(opt.cfg).to(device)
	model.train()

	#profile
	if opt.profile:
		img = torch.rand(8 if torch.cuda.is_available() else 1,3,640,640).to(device)
		y=model(img,profile=True)
